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A nonlinear level set model for image deblurring and denoising

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Abstract

Image deblurring and denoising are fundamental problems in the field of image processing with numerous applications. This paper presents a new nonlinear Partial Differential Equation (PDE) model based on curve evolution via level sets, for recovering images from their blurry and noisy observations. The proposed method integrates an image deconvolution process and a curve evolution based regularizing process to form a reaction-diffusion PDE. The regularization term in the proposed PDE is a combination of a diffusive image smoothing term and a reactive image enhancement term. The diffusive and reactive terms present in the model lead to effective suppression of noise with sharp restoration of image features. We present several numerical results for image restoration, with synthetic and real degradations and compare it to other state-of-the-art image restoration techniques. The experiments confirm the favorable performance of our method, both visually and in terms of Improvement in Signal-to-Noise-Ratio (ISNR) and Pratt’s Figure Of Merit (FOM).

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    A. A. Bini & M. S. Bhat

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  1. A. A. Bini
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Correspondence to A. A. Bini.

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Bini, A.A., Bhat, M.S. A nonlinear level set model for image deblurring and denoising. Vis Comput 30, 311–325 (2014). https://doi.org/10.1007/s00371-013-0857-6

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